






"""simple docstring"""


import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__SCREAMING_SNAKE_CASE   =   {
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
						assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
						if args.mlm:
												assert os.path.isfile(args.token_counts )
												assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
						else:
												assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

						assert args.teacher_type == args.student_type or (
						    args.student_type == "distilbert" and args.teacher_type == "bert"
						)
						assert os.path.isfile(args.student_config )
						if args.student_pretrained_weights is not None:
												assert os.path.isfile(args.student_pretrained_weights )

						if args.freeze_token_type_embds:
												assert args.student_type in ["roberta"]

						assert args.alpha_ce >= 0.0
						assert args.alpha_mlm >= 0.0
						assert args.alpha_clm >= 0.0
						assert args.alpha_mse >= 0.0
						assert args.alpha_cos >= 0.0
						assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if args.student_type == "roberta":
												lowerCAmelCase   :str						=			False
						elif args.student_type == "gpt2":
												lowerCAmelCase   :Optional[int]						=			False


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if args.student_type == "roberta":
												lowerCAmelCase   :Optional[Any]						=			False


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			argparse.ArgumentParser(description='Training' )
						parser.add_argument('--force'     ,      action='store_true'     ,      help='Overwrite dump_path if it already exists.' )

						parser.add_argument(
						    '--dump_path'     ,      type=a__     ,      required=a__     ,      help='The output directory (log, checkpoints, parameters, etc.)' )
						parser.add_argument(
						    '--data_file'     ,      type=a__     ,      required=a__     ,      help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'     ,      )

						parser.add_argument(
						    '--student_type'     ,      type=a__     ,      choices=['distilbert', 'roberta', 'gpt2']     ,      required=a__     ,      help='The student type (DistilBERT, RoBERTa).'     ,      )
						parser.add_argument('--student_config'     ,      type=a__     ,      required=a__     ,      help='Path to the student configuration.' )
						parser.add_argument(
						    '--student_pretrained_weights'     ,      default=a__     ,      type=a__     ,      help='Load student initialization checkpoint.' )

						parser.add_argument(
						    '--teacher_type'     ,      choices=['bert', 'roberta', 'gpt2']     ,      required=a__     ,      help='Teacher type (BERT, RoBERTa).' )
						parser.add_argument('--teacher_name'     ,      type=a__     ,      required=a__     ,      help='The teacher model.' )

						parser.add_argument('--temperature'     ,      default=2.0     ,      type=a__     ,      help='Temperature for the softmax temperature.' )
						parser.add_argument(
						    '--alpha_ce'     ,      default=0.5     ,      type=a__     ,      help='Linear weight for the distillation loss. Must be >=0.' )
						parser.add_argument(
						    '--alpha_mlm'     ,      default=0.0     ,      type=a__     ,      help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'     ,      )
						parser.add_argument('--alpha_clm'     ,      default=0.5     ,      type=a__     ,      help='Linear weight for the CLM loss. Must be >=0.' )
						parser.add_argument('--alpha_mse'     ,      default=0.0     ,      type=a__     ,      help='Linear weight of the MSE loss. Must be >=0.' )
						parser.add_argument(
						    '--alpha_cos'     ,      default=0.0     ,      type=a__     ,      help='Linear weight of the cosine embedding loss. Must be >=0.' )

						parser.add_argument(
						    '--mlm'     ,      action='store_true'     ,      help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.' )
						parser.add_argument(
						    '--mlm_mask_prop'     ,      default=0.15     ,      type=a__     ,      help='Proportion of tokens for which we need to make a prediction.'     ,      )
						parser.add_argument('--word_mask'     ,      default=0.8     ,      type=a__     ,      help='Proportion of tokens to mask out.' )
						parser.add_argument('--word_keep'     ,      default=0.1     ,      type=a__     ,      help='Proportion of tokens to keep.' )
						parser.add_argument('--word_rand'     ,      default=0.1     ,      type=a__     ,      help='Proportion of tokens to randomly replace.' )
						parser.add_argument(
						    '--mlm_smoothing'     ,      default=0.7     ,      type=a__     ,      help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'     ,      )
						parser.add_argument('--token_counts'     ,      type=a__     ,      help='The token counts in the data_file for MLM.' )

						parser.add_argument(
						    '--restrict_ce_to_mask'     ,      action='store_true'     ,      help='If true, compute the distillation loss only the [MLM] prediction distribution.'     ,      )
						parser.add_argument(
						    '--freeze_pos_embs'     ,      action='store_true'     ,      help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'     ,      )
						parser.add_argument(
						    '--freeze_token_type_embds'     ,      action='store_true'     ,      help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'     ,      )

						parser.add_argument('--n_epoch'     ,      type=a__     ,      default=3     ,      help='Number of pass on the whole dataset.' )
						parser.add_argument('--batch_size'     ,      type=a__     ,      default=5     ,      help='Batch size (for each process).' )
						parser.add_argument(
						    '--group_by_size'     ,      action='store_false'     ,      help='If true, group sequences that have similar length into the same batch. Default is true.'     ,      )

						parser.add_argument(
						    '--gradient_accumulation_steps'     ,      type=a__     ,      default=50     ,      help='Gradient accumulation for larger training batches.'     ,      )
						parser.add_argument('--warmup_prop'     ,      default=0.05     ,      type=a__     ,      help='Linear warmup proportion.' )
						parser.add_argument('--weight_decay'     ,      default=0.0     ,      type=a__     ,      help='Weight decay if we apply some.' )
						parser.add_argument('--learning_rate'     ,      default=5e-4     ,      type=a__     ,      help='The initial learning rate for Adam.' )
						parser.add_argument('--adam_epsilon'     ,      default=1e-6     ,      type=a__     ,      help='Epsilon for Adam optimizer.' )
						parser.add_argument('--max_grad_norm'     ,      default=5.0     ,      type=a__     ,      help='Max gradient norm.' )
						parser.add_argument('--initializer_range'     ,      default=0.02     ,      type=a__     ,      help='Random initialization range.' )

						parser.add_argument(
						    '--fp16'     ,      action='store_true'     ,      help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'     ,      )
						parser.add_argument(
						    '--fp16_opt_level'     ,      type=a__     ,      default='O1'     ,      help=(
						        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
						        'See details at https://nvidia.github.io/apex/amp.html'
						    )     ,      )
						parser.add_argument('--n_gpu'     ,      type=a__     ,      default=1     ,      help='Number of GPUs in the node.' )
						parser.add_argument('--local_rank'     ,      type=a__     ,      default=-1     ,      help='Distributed training - Local rank' )
						parser.add_argument('--seed'     ,      type=a__     ,      default=56     ,      help='Random seed' )

						parser.add_argument('--log_interval'     ,      type=a__     ,      default=5_00     ,      help='Tensorboard logging interval.' )
						parser.add_argument('--checkpoint_interval'     ,      type=a__     ,      default=40_00     ,      help='Checkpoint interval.' )
						lowerCAmelCase   :Optional[Any]						=			parser.parse_args()
						sanity_checks(a__ )

						# ARGS #
						init_gpu_params(a__ )
						set_seed(a__ )
						if args.is_master:
												if os.path.exists(args.dump_path ):
																		if not args.force:
																								raise ValueError(
																								    F"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
																								    ' itUse `--force` if you want to overwrite it' )
																		else:
																								shutil.rmtree(args.dump_path )

												if not os.path.exists(args.dump_path ):
																		os.makedirs(args.dump_path )
												logger.info(F"""Experiment will be dumped and logged in {args.dump_path}""" )

												# SAVE PARAMS #
												logger.info(F"""Param: {args}""" )
												with open(os.path.join(args.dump_path     ,      'parameters.json' )     ,      'w' ) as f:
																		json.dump(vars(a__ )     ,      a__     ,      indent=4 )
												git_log(args.dump_path )

						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			MODEL_CLASSES[args.student_type]
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Any						=			MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						lowerCAmelCase   :Optional[Any]						=			teacher_tokenizer_class.from_pretrained(args.teacher_name )
						lowerCAmelCase   :Tuple						=			{}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												lowerCAmelCase   :List[Any]						=			tokenizer.all_special_tokens.index(a__ )
												lowerCAmelCase   :Dict						=			tokenizer.all_special_ids[idx]
						logger.info(F"""Special tokens {special_tok_ids}""" )
						lowerCAmelCase   :str						=			special_tok_ids
						lowerCAmelCase   :str						=			tokenizer.max_model_input_sizes[args.teacher_name]

						# DATA LOADER #
						logger.info(F"""Loading data from {args.data_file}""" )
						with open(args.data_file     ,      'rb' ) as fp:
												lowerCAmelCase   :Optional[Any]						=			pickle.load(a__ )

						if args.mlm:
												logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)""" )
												with open(args.token_counts     ,      'rb' ) as fp:
																		lowerCAmelCase   :List[Any]						=			pickle.load(a__ )

												lowerCAmelCase   :int						=			np.maximum(a__     ,      1 ) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		lowerCAmelCase   :List[str]						=			0.0  # do not predict special tokens
												lowerCAmelCase   :Any						=			torch.from_numpy(a__ )
						else:
												lowerCAmelCase   :Optional[Any]						=			None

						lowerCAmelCase   :Dict						=			LmSeqsDataset(params=a__     ,      data=a__ )
						logger.info('Data loader created.' )

						# STUDENT #
						logger.info(F"""Loading student config from {args.student_config}""" )
						lowerCAmelCase   :Dict						=			student_config_class.from_pretrained(args.student_config )
						lowerCAmelCase   :Dict						=			True

						if args.student_pretrained_weights is not None:
												logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}""" )
												lowerCAmelCase   :List[Any]						=			student_model_class.from_pretrained(args.student_pretrained_weights     ,      config=a__ )
						else:
												lowerCAmelCase   :Dict						=			student_model_class(a__ )

						if args.n_gpu > 0:
												student.to(F"""cuda:{args.local_rank}""" )
						logger.info('Student loaded.' )

						# TEACHER #
						lowerCAmelCase   :List[str]						=			teacher_model_class.from_pretrained(args.teacher_name     ,      output_hidden_states=a__ )
						if args.n_gpu > 0:
												teacher.to(F"""cuda:{args.local_rank}""" )
						logger.info(F"""Teacher loaded from {args.teacher_name}.""" )

						# FREEZING #
						if args.freeze_pos_embs:
												freeze_pos_embeddings(a__     ,      a__ )
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(a__     ,      a__ )

						# SANITY CHECKS #
						assert student.config.vocab_size == teacher.config.vocab_size
						assert student.config.hidden_size == teacher.config.hidden_size
						assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
						if args.mlm:
												assert token_probs.size(0 ) == stu_architecture_config.vocab_size

						# DISTILLER #
						torch.cuda.empty_cache()
						lowerCAmelCase   :Optional[Any]						=			Distiller(
						    params=a__     ,      dataset=a__     ,      token_probs=a__     ,      student=a__     ,      teacher=a__ )
						distiller.train()
						logger.info('Let\'s go get some drinks.' )


if __name__ == "__main__":
		main()







"""simple docstring"""


import math


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						if 0 not in (x, y):
												# We use the relation x^y = y*log10(x), where 10 is the base.
												return y * math.logaa(a__ )
						else:
												if x == 0:  # 0 raised to any number is 0
																		return 0
												elif y == 0:
																		return 1  # any number raised to 0 is 1
						raise AssertionError('This should never happen' )


if __name__ == "__main__":  # Main function
		# Read two numbers from input and typecast them to int using map function.
		# Here x is the base and y is the power.
		__SCREAMING_SNAKE_CASE   =   'Enter the base and the power separated by a comma: '
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   map(int, input(prompt).split(','))
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   map(int, input(prompt).split(','))

		# We find the log of each number, using the function res(), which takes two
		# arguments.
		__SCREAMING_SNAKE_CASE   =   res(xa, ya)
		__SCREAMING_SNAKE_CASE   =   res(xa, ya)

		# We check for the largest number
		if resa > resa:
				print('Largest number is', xa, '^', ya)
		elif resa > resa:
				print('Largest number is', xa, '^', ya)
		else:
				print('Both are equal')